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<h1 id="the-bayesian-models-module">The <code>bayesian models</code> module<a class="headerlink" href="#the-bayesian-models-module" title="Permanent link">&para;</a></h1>
<p>This module contains the two Bayesian Models available in this library, namely
the bayesian version of the <code>Wide</code> and <code>TabMlp</code> models, referred as
<code>BayesianWide</code> and <code>BayesianTabMlp</code>. These models are very useful in
scenarios where getting a measure of uncertainty is important.</p>
<p>The models in this module are based on the publication:
<a href="https://arxiv.org/abs/1505.05424?context=cs">Weight Uncertainty in Neural Networks</a>.</p>


<div class="doc doc-object doc-class">



<h2 id="pytorch_widedeep.bayesian_models.tabular.bayesian_linear.bayesian_wide.BayesianWide" class="doc doc-heading">
            <span class="doc doc-object-name doc-class-name">BayesianWide</span>


<a href="#pytorch_widedeep.bayesian_models.tabular.bayesian_linear.bayesian_wide.BayesianWide" class="headerlink" title="Permanent link">&para;</a></h2>


    <div class="doc doc-contents first">
            <p class="doc doc-class-bases">
              Bases: <code><span title="pytorch_widedeep.bayesian_models._base_bayesian_model.BaseBayesianModel">BaseBayesianModel</span></code></p>


        <p>Defines a <code>Wide</code> model. This is a linear model where the
non-linearlities are captured via crossed-columns</p>


<p><span class="doc-section-title">Parameters:</span></p>
    <table>
      <thead>
        <tr>
          <th>Name</th>
          <th>Type</th>
          <th>Description</th>
          <th>Default</th>
        </tr>
      </thead>
      <tbody>
          <tr class="doc-section-item">
            <td>
                <code>input_dim</code>
            </td>
            <td>
                  <code>int</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>size of the Embedding layer. <code>input_dim</code> is the summation of all the
individual values for all the features that go through the wide
component. For example, if the wide component receives 2 features with
5 individual values each, <code>input_dim = 10</code></p>
              </div>
            </td>
            <td>
                <em>required</em>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>pred_dim</code>
            </td>
            <td>
                  <code>int</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>size of the ouput tensor containing the predictions</p>
              </div>
            </td>
            <td>
                  <code>1</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>prior_sigma_1</code>
            </td>
            <td>
                  <code>float</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>The prior weight distribution is a scaled mixture of two Gaussian
densities:</p>
<div class="arithmatex">\[
   \begin{aligned}
   P(\mathbf{w}) = \prod_{i=j} \pi N (\mathbf{w}_j | 0, \sigma_{1}^{2}) + (1 - \pi) N (\mathbf{w}_j | 0, \sigma_{2}^{2})
   \end{aligned}
\]</div>
<p><code>prior_sigma_1</code> is the prior of the sigma parameter for the first of the two
Gaussians that will be mixed to produce the prior weight
distribution.</p>
              </div>
            </td>
            <td>
                  <code>1.0</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>prior_sigma_2</code>
            </td>
            <td>
                  <code>float</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>Prior of the sigma parameter for the second of the two Gaussian
distributions that will be mixed to produce the prior weight
distribution</p>
              </div>
            </td>
            <td>
                  <code>0.002</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>prior_pi</code>
            </td>
            <td>
                  <code>float</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>Scaling factor that will be used to mix the Gaussians to produce the
prior weight distribution</p>
              </div>
            </td>
            <td>
                  <code>0.8</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>posterior_mu_init</code>
            </td>
            <td>
                  <code>float</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>The posterior sample of the weights is defined as:</p>
<div class="arithmatex">\[
   \begin{aligned}
   \mathbf{w} &amp;= \mu + log(1 + exp(\rho))
   \end{aligned}
\]</div>
<p>where:</p>
<div class="arithmatex">\[
   \begin{aligned}
   \mathcal{N}(x\vert \mu, \sigma) &amp;= \frac{1}{\sqrt{2\pi}\sigma}e^{-\frac{(x-\mu)^2}{2\sigma^2}}\\
   \log{\mathcal{N}(x\vert \mu, \sigma)} &amp;= -\log{\sqrt{2\pi}} -\log{\sigma} -\frac{(x-\mu)^2}{2\sigma^2}\\
   \end{aligned}
\]</div>
<p><span class="arithmatex">\(\mu\)</span> is initialised using a normal distributtion with mean
<code>posterior_mu_init</code> and std equal to 0.1.</p>
              </div>
            </td>
            <td>
                  <code>0.0</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>posterior_rho_init</code>
            </td>
            <td>
                  <code>float</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>As in the case of <span class="arithmatex">\(\mu\)</span>, <span class="arithmatex">\(\rho\)</span> is initialised using a
normal distributtion with mean <code>posterior_rho_init</code> and std equal to
0.1.</p>
              </div>
            </td>
            <td>
                  <code>-7.0</code>
            </td>
          </tr>
      </tbody>
    </table>


<p><span class="doc-section-title">Attributes:</span></p>
    <table>
      <thead>
        <tr>
          <th>Name</th>
          <th>Type</th>
          <th>Description</th>
        </tr>
      </thead>
      <tbody>
          <tr class="doc-section-item">
            <td><code><span title="pytorch_widedeep.bayesian_models.tabular.bayesian_linear.bayesian_wide.BayesianWide.bayesian_wide_linear">bayesian_wide_linear</span></code></td>
            <td>
                  <code><span title="torch.nn.Module">Module</span></code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>the linear layer that comprises the wide branch of the model</p>
              </div>
            </td>
          </tr>
      </tbody>
    </table>


<p><span class="doc-section-title">Examples:</span></p>
    <div class="highlight"><pre><span></span><code><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">torch</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">pytorch_widedeep.bayesian_models</span> <span class="kn">import</span> <span class="n">BayesianWide</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">random_</span><span class="p">(</span><span class="mi">6</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">wide</span> <span class="o">=</span> <span class="n">BayesianWide</span><span class="p">(</span><span class="n">input_dim</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()),</span> <span class="n">pred_dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span> <span class="o">=</span> <span class="n">wide</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
</code></pre></div>






              <details class="quote">
                <summary>Source code in <code>pytorch_widedeep/bayesian_models/tabular/bayesian_linear/bayesian_wide.py</code></summary>
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<span class="normal">107</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">class</span> <span class="nc">BayesianWide</span><span class="p">(</span><span class="n">BaseBayesianModel</span><span class="p">):</span>
<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Defines a `Wide` model. This is a linear model where the</span>
<span class="sd">    non-linearlities are captured via crossed-columns</span>

<span class="sd">    Parameters</span>
<span class="sd">    ----------</span>
<span class="sd">    input_dim: int</span>
<span class="sd">        size of the Embedding layer. `input_dim` is the summation of all the</span>
<span class="sd">        individual values for all the features that go through the wide</span>
<span class="sd">        component. For example, if the wide component receives 2 features with</span>
<span class="sd">        5 individual values each, `input_dim = 10`</span>
<span class="sd">    pred_dim: int</span>
<span class="sd">        size of the ouput tensor containing the predictions</span>
<span class="sd">    prior_sigma_1: float, default = 1.0</span>
<span class="sd">        The prior weight distribution is a scaled mixture of two Gaussian</span>
<span class="sd">        densities:</span>

<span class="sd">        $$</span>
<span class="sd">           \begin{aligned}</span>
<span class="sd">           P(\mathbf{w}) = \prod_{i=j} \pi N (\mathbf{w}_j | 0, \sigma_{1}^{2}) + (1 - \pi) N (\mathbf{w}_j | 0, \sigma_{2}^{2})</span>
<span class="sd">           \end{aligned}</span>
<span class="sd">        $$</span>

<span class="sd">        `prior_sigma_1` is the prior of the sigma parameter for the first of the two</span>
<span class="sd">        Gaussians that will be mixed to produce the prior weight</span>
<span class="sd">        distribution.</span>
<span class="sd">    prior_sigma_2: float, default = 0.002</span>
<span class="sd">        Prior of the sigma parameter for the second of the two Gaussian</span>
<span class="sd">        distributions that will be mixed to produce the prior weight</span>
<span class="sd">        distribution</span>
<span class="sd">    prior_pi: float, default = 0.8</span>
<span class="sd">        Scaling factor that will be used to mix the Gaussians to produce the</span>
<span class="sd">        prior weight distribution</span>
<span class="sd">    posterior_mu_init: float = 0.0</span>
<span class="sd">        The posterior sample of the weights is defined as:</span>

<span class="sd">        $$</span>
<span class="sd">           \begin{aligned}</span>
<span class="sd">           \mathbf{w} &amp;= \mu + log(1 + exp(\rho))</span>
<span class="sd">           \end{aligned}</span>
<span class="sd">        $$</span>

<span class="sd">        where:</span>

<span class="sd">        $$</span>
<span class="sd">           \begin{aligned}</span>
<span class="sd">           \mathcal{N}(x\vert \mu, \sigma) &amp;= \frac{1}{\sqrt{2\pi}\sigma}e^{-\frac{(x-\mu)^2}{2\sigma^2}}\\</span>
<span class="sd">           \log{\mathcal{N}(x\vert \mu, \sigma)} &amp;= -\log{\sqrt{2\pi}} -\log{\sigma} -\frac{(x-\mu)^2}{2\sigma^2}\\</span>
<span class="sd">           \end{aligned}</span>
<span class="sd">        $$</span>

<span class="sd">        $\mu$ is initialised using a normal distributtion with mean</span>
<span class="sd">        `posterior_mu_init` and std equal to 0.1.</span>
<span class="sd">    posterior_rho_init: float = -7.0</span>
<span class="sd">        As in the case of $\mu$, $\rho$ is initialised using a</span>
<span class="sd">        normal distributtion with mean `posterior_rho_init` and std equal to</span>
<span class="sd">        0.1.</span>

<span class="sd">    Attributes</span>
<span class="sd">    -----------</span>
<span class="sd">    bayesian_wide_linear: nn.Module</span>
<span class="sd">        the linear layer that comprises the wide branch of the model</span>

<span class="sd">    Examples</span>
<span class="sd">    --------</span>
<span class="sd">    &gt;&gt;&gt; import torch</span>
<span class="sd">    &gt;&gt;&gt; from pytorch_widedeep.bayesian_models import BayesianWide</span>
<span class="sd">    &gt;&gt;&gt; X = torch.empty(4, 4).random_(6)</span>
<span class="sd">    &gt;&gt;&gt; wide = BayesianWide(input_dim=int(X.max().item()), pred_dim=1)</span>
<span class="sd">    &gt;&gt;&gt; out = wide(X)</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
        <span class="bp">self</span><span class="p">,</span>
        <span class="n">input_dim</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
        <span class="n">pred_dim</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
        <span class="n">prior_sigma_1</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span>
        <span class="n">prior_sigma_2</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.002</span><span class="p">,</span>
        <span class="n">prior_pi</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.8</span><span class="p">,</span>
        <span class="n">posterior_mu_init</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.0</span><span class="p">,</span>
        <span class="n">posterior_rho_init</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="o">-</span><span class="mf">7.0</span><span class="p">,</span>
    <span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">BayesianWide</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="c1">#  Embeddings: val + 1 because 0 is reserved for padding/unseen cateogories.</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">bayesian_wide_linear</span> <span class="o">=</span> <span class="n">bnn</span><span class="o">.</span><span class="n">BayesianEmbedding</span><span class="p">(</span>
            <span class="n">n_embed</span><span class="o">=</span><span class="n">input_dim</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span>
            <span class="n">embed_dim</span><span class="o">=</span><span class="n">pred_dim</span><span class="p">,</span>
            <span class="n">padding_idx</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
            <span class="n">prior_sigma_1</span><span class="o">=</span><span class="n">prior_sigma_1</span><span class="p">,</span>
            <span class="n">prior_sigma_2</span><span class="o">=</span><span class="n">prior_sigma_2</span><span class="p">,</span>
            <span class="n">prior_pi</span><span class="o">=</span><span class="n">prior_pi</span><span class="p">,</span>
            <span class="n">posterior_mu_init</span><span class="o">=</span><span class="n">posterior_mu_init</span><span class="p">,</span>
            <span class="n">posterior_rho_init</span><span class="o">=</span><span class="n">posterior_rho_init</span><span class="p">,</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">pred_dim</span><span class="p">))</span>

    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bayesian_wide_linear</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">long</span><span class="p">())</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span>
        <span class="k">return</span> <span class="n">out</span>
</code></pre></div></td></tr></table></div>
              </details>



  <div class="doc doc-children">











  </div>

    </div>

</div>

<div class="doc doc-object doc-class">



<h2 id="pytorch_widedeep.bayesian_models.tabular.bayesian_mlp.bayesian_tab_mlp.BayesianTabMlp" class="doc doc-heading">
            <span class="doc doc-object-name doc-class-name">BayesianTabMlp</span>


<a href="#pytorch_widedeep.bayesian_models.tabular.bayesian_mlp.bayesian_tab_mlp.BayesianTabMlp" class="headerlink" title="Permanent link">&para;</a></h2>


    <div class="doc doc-contents first">
            <p class="doc doc-class-bases">
              Bases: <code><span title="pytorch_widedeep.bayesian_models._base_bayesian_model.BaseBayesianModel">BaseBayesianModel</span></code></p>


        <p>Defines a <code>BayesianTabMlp</code> model.</p>
<p>This class combines embedding representations of the categorical features
with numerical (aka continuous) features, embedded or not. These are then
passed through a series of probabilistic dense layers (i.e. a MLP).</p>


<p><span class="doc-section-title">Parameters:</span></p>
    <table>
      <thead>
        <tr>
          <th>Name</th>
          <th>Type</th>
          <th>Description</th>
          <th>Default</th>
        </tr>
      </thead>
      <tbody>
          <tr class="doc-section-item">
            <td>
                <code>column_idx</code>
            </td>
            <td>
                  <code><span title="pytorch_widedeep.wdtypes.Dict">Dict</span>[str, int]</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>Dict containing the index of the columns that will be passed through
the <code>TabMlp</code> model. Required to slice the tensors. e.g. <em>{'education':
0, 'relationship': 1, 'workclass': 2, ...}</em></p>
              </div>
            </td>
            <td>
                <em>required</em>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>cat_embed_input</code>
            </td>
            <td>
                  <code><span title="pytorch_widedeep.wdtypes.Optional">Optional</span>[<span title="pytorch_widedeep.wdtypes.List">List</span>[<span title="pytorch_widedeep.wdtypes.Tuple">Tuple</span>[str, int, int]]]</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>List of Tuples with the column name, number of unique values and
embedding dimension. e.g. <em>[(education, 11, 32), ...]</em></p>
              </div>
            </td>
            <td>
                  <code>None</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>cat_embed_activation</code>
            </td>
            <td>
                  <code><span title="pytorch_widedeep.wdtypes.Optional">Optional</span>[str]</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>Activation function for the categorical embeddings, if any. Currently
<em>'tanh'</em>, <em>'relu'</em>, <em>'leaky_relu'</em> and <em>'gelu'</em> are supported</p>
              </div>
            </td>
            <td>
                  <code>None</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>continuous_cols</code>
            </td>
            <td>
                  <code><span title="pytorch_widedeep.wdtypes.Optional">Optional</span>[<span title="pytorch_widedeep.wdtypes.List">List</span>[str]]</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>List with the name of the numeric (aka continuous) columns</p>
              </div>
            </td>
            <td>
                  <code>None</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>cont_norm_layer</code>
            </td>
            <td>
                  <code><span title="pytorch_widedeep.wdtypes.Optional">Optional</span>[<span title="pytorch_widedeep.wdtypes.Literal">Literal</span>[batchnorm, layernorm]]</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>Type of normalization layer applied to the continuous features. Options
are: 'layernorm', 'batchnorm' or None.</p>
              </div>
            </td>
            <td>
                  <code>None</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>embed_continuous</code>
            </td>
            <td>
                  <code><span title="pytorch_widedeep.wdtypes.Optional">Optional</span>[bool]</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>Boolean indicating if the continuous columns will be embedded
(i.e. passed each through a linear layer with or without activation)</p>
              </div>
            </td>
            <td>
                  <code>None</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>cont_embed_dim</code>
            </td>
            <td>
                  <code><span title="pytorch_widedeep.wdtypes.Optional">Optional</span>[int]</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>Size of the continuous embeddings</p>
              </div>
            </td>
            <td>
                  <code>None</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>cont_embed_dropout</code>
            </td>
            <td>
                  <code><span title="pytorch_widedeep.wdtypes.Optional">Optional</span>[float]</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>Dropout for the continuous embeddings</p>
              </div>
            </td>
            <td>
                  <code>None</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>use_cont_bias</code>
            </td>
            <td>
                  <code><span title="pytorch_widedeep.wdtypes.Optional">Optional</span>[bool]</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>Boolean indicating if bias will be used for the continuous embeddings</p>
              </div>
            </td>
            <td>
                  <code>None</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>cont_embed_activation</code>
            </td>
            <td>
                  <code><span title="pytorch_widedeep.wdtypes.Optional">Optional</span>[str]</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>Activation function for the continuous embeddings if any. Currently
<em>'tanh'</em>, <em>'relu'</em>, <em>'leaky_relu'</em> and <em>'gelu'</em> are supported</p>
              </div>
            </td>
            <td>
                  <code>None</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>mlp_hidden_dims</code>
            </td>
            <td>
                  <code><span title="pytorch_widedeep.wdtypes.List">List</span>[int]</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>List with the number of neurons per dense layer in the mlp.</p>
              </div>
            </td>
            <td>
                  <code>[200, 100]</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>mlp_activation</code>
            </td>
            <td>
                  <code>str</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>Activation function for the dense layers of the MLP. Currently
<em>'tanh'</em>, <em>'relu'</em>, <em>'leaky_relu'</em> and <em>'gelu'</em> are supported</p>
              </div>
            </td>
            <td>
                  <code>&#39;leaky_relu&#39;</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>prior_sigma_1</code>
            </td>
            <td>
                  <code>float</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>The prior weight distribution is a scaled mixture of two Gaussian
densities:</p>
<div class="arithmatex">\[
   \begin{aligned}
   P(\mathbf{w}) = \prod_{i=j} \pi N (\mathbf{w}_j | 0, \sigma_{1}^{2}) + (1 - \pi) N (\mathbf{w}_j | 0, \sigma_{2}^{2})
   \end{aligned}
\]</div>
<p><code>prior_sigma_1</code> is the prior of the sigma parameter for the first of the two
Gaussians that will be mixed to produce the prior weight
distribution.</p>
              </div>
            </td>
            <td>
                  <code>1</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>prior_sigma_2</code>
            </td>
            <td>
                  <code>float</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>Prior of the sigma parameter for the second of the two Gaussian
distributions that will be mixed to produce the prior weight
distribution for each Bayesian linear and embedding layer</p>
              </div>
            </td>
            <td>
                  <code>0.002</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>prior_pi</code>
            </td>
            <td>
                  <code>float</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>Scaling factor that will be used to mix the Gaussians to produce the
prior weight distribution ffor each Bayesian linear and embedding
layer</p>
              </div>
            </td>
            <td>
                  <code>0.8</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>posterior_mu_init</code>
            </td>
            <td>
                  <code>float</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>The posterior sample of the weights is defined as:</p>
<p>$$
   \begin{aligned}
   \mathbf{w} &amp;= \mu + log(1 + exp(\rho))
   \end{aligned}
$$
where:</p>
<div class="arithmatex">\[
   \begin{aligned}
   \mathcal{N}(x\vert \mu, \sigma) &amp;= \frac{1}{\sqrt{2\pi}\sigma}e^{-\frac{(x-\mu)^2}{2\sigma^2}}\\
   \log{\mathcal{N}(x\vert \mu, \sigma)} &amp;= -\log{\sqrt{2\pi}} -\log{\sigma} -\frac{(x-\mu)^2}{2\sigma^2}\\
   \end{aligned}
\]</div>
<p><span class="arithmatex">\(\mu\)</span> is initialised using a normal distributtion with mean
<code>posterior_mu_init</code> and std equal to 0.1.</p>
              </div>
            </td>
            <td>
                  <code>0.0</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>posterior_rho_init</code>
            </td>
            <td>
                  <code>float</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>As in the case of <span class="arithmatex">\(\mu\)</span>, <span class="arithmatex">\(\rho\)</span> is initialised using a
normal distributtion with mean <code>posterior_rho_init</code> and std equal to
0.1.</p>
              </div>
            </td>
            <td>
                  <code>-7.0</code>
            </td>
          </tr>
      </tbody>
    </table>


<p><span class="doc-section-title">Attributes:</span></p>
    <table>
      <thead>
        <tr>
          <th>Name</th>
          <th>Type</th>
          <th>Description</th>
        </tr>
      </thead>
      <tbody>
          <tr class="doc-section-item">
            <td><code><span title="pytorch_widedeep.bayesian_models.tabular.bayesian_mlp.bayesian_tab_mlp.BayesianTabMlp.bayesian_cat_and_cont_embed">bayesian_cat_and_cont_embed</span></code></td>
            <td>
                  <code><span title="torch.nn.Module">Module</span></code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>This is the module that processes the categorical and continuous columns</p>
              </div>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td><code><span title="pytorch_widedeep.bayesian_models.tabular.bayesian_mlp.bayesian_tab_mlp.BayesianTabMlp.bayesian_tab_mlp">bayesian_tab_mlp</span></code></td>
            <td>
                  <code><span title="torch.nn.Sequential">Sequential</span></code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>mlp model that will receive the concatenation of the embeddings and
the continuous columns</p>
              </div>
            </td>
          </tr>
      </tbody>
    </table>


<p><span class="doc-section-title">Examples:</span></p>
    <div class="highlight"><pre><span></span><code><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">torch</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">pytorch_widedeep.bayesian_models</span> <span class="kn">import</span> <span class="n">BayesianTabMlp</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X_tab</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">random_</span><span class="p">(</span><span class="mi">4</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">colnames</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">,</span> <span class="s1">&#39;e&#39;</span><span class="p">]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">cat_embed_input</span> <span class="o">=</span> <span class="p">[(</span><span class="n">u</span><span class="p">,</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">)</span> <span class="k">for</span> <span class="n">u</span><span class="p">,</span><span class="n">i</span><span class="p">,</span><span class="n">j</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">colnames</span><span class="p">[:</span><span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">]</span><span class="o">*</span><span class="mi">4</span><span class="p">,</span> <span class="p">[</span><span class="mi">8</span><span class="p">]</span><span class="o">*</span><span class="mi">4</span><span class="p">)]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">column_idx</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span><span class="n">v</span> <span class="k">for</span> <span class="n">v</span><span class="p">,</span><span class="n">k</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">colnames</span><span class="p">)}</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span> <span class="o">=</span> <span class="n">BayesianTabMlp</span><span class="p">(</span><span class="n">mlp_hidden_dims</span><span class="o">=</span><span class="p">[</span><span class="mi">8</span><span class="p">,</span><span class="mi">4</span><span class="p">],</span> <span class="n">column_idx</span><span class="o">=</span><span class="n">column_idx</span><span class="p">,</span> <span class="n">cat_embed_input</span><span class="o">=</span><span class="n">cat_embed_input</span><span class="p">,</span>
<span class="gp">... </span><span class="n">continuous_cols</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;e&#39;</span><span class="p">])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">X_tab</span><span class="p">)</span>
</code></pre></div>






              <details class="quote">
                <summary>Source code in <code>pytorch_widedeep/bayesian_models/tabular/bayesian_mlp/bayesian_tab_mlp.py</code></summary>
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<span class="normal">263</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">class</span> <span class="nc">BayesianTabMlp</span><span class="p">(</span><span class="n">BaseBayesianModel</span><span class="p">):</span>
<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Defines a `BayesianTabMlp` model.</span>

<span class="sd">    This class combines embedding representations of the categorical features</span>
<span class="sd">    with numerical (aka continuous) features, embedded or not. These are then</span>
<span class="sd">    passed through a series of probabilistic dense layers (i.e. a MLP).</span>

<span class="sd">    Parameters</span>
<span class="sd">    ----------</span>
<span class="sd">    column_idx: Dict</span>
<span class="sd">        Dict containing the index of the columns that will be passed through</span>
<span class="sd">        the `TabMlp` model. Required to slice the tensors. e.g. _{&#39;education&#39;:</span>
<span class="sd">        0, &#39;relationship&#39;: 1, &#39;workclass&#39;: 2, ...}_</span>
<span class="sd">    cat_embed_input: List, Optional, default = None</span>
<span class="sd">        List of Tuples with the column name, number of unique values and</span>
<span class="sd">        embedding dimension. e.g. _[(education, 11, 32), ...]_</span>
<span class="sd">    cat_embed_activation: Optional, str, default = None,</span>
<span class="sd">        Activation function for the categorical embeddings, if any. Currently</span>
<span class="sd">        _&#39;tanh&#39;_, _&#39;relu&#39;_, _&#39;leaky_relu&#39;_ and _&#39;gelu&#39;_ are supported</span>
<span class="sd">    continuous_cols: List, Optional, default = None</span>
<span class="sd">        List with the name of the numeric (aka continuous) columns</span>
<span class="sd">    cont_norm_layer: str, default =  &quot;batchnorm&quot;</span>
<span class="sd">        Type of normalization layer applied to the continuous features. Options</span>
<span class="sd">        are: &#39;layernorm&#39;, &#39;batchnorm&#39; or None.</span>
<span class="sd">    embed_continuous: bool, default = False,</span>
<span class="sd">        Boolean indicating if the continuous columns will be embedded</span>
<span class="sd">        (i.e. passed each through a linear layer with or without activation)</span>
<span class="sd">    cont_embed_dim: int, default = 32,</span>
<span class="sd">        Size of the continuous embeddings</span>
<span class="sd">    cont_embed_dropout: float, default = 0.1,</span>
<span class="sd">        Dropout for the continuous embeddings</span>
<span class="sd">    use_cont_bias: bool, default = True,</span>
<span class="sd">        Boolean indicating if bias will be used for the continuous embeddings</span>
<span class="sd">    cont_embed_activation: Optional, str, default = None,</span>
<span class="sd">        Activation function for the continuous embeddings if any. Currently</span>
<span class="sd">        _&#39;tanh&#39;_, _&#39;relu&#39;_, _&#39;leaky_relu&#39;_ and _&#39;gelu&#39;_ are supported</span>
<span class="sd">    mlp_hidden_dims: List, default = [200, 100]</span>
<span class="sd">        List with the number of neurons per dense layer in the mlp.</span>
<span class="sd">    mlp_activation: str, default = &quot;relu&quot;</span>
<span class="sd">        Activation function for the dense layers of the MLP. Currently</span>
<span class="sd">        _&#39;tanh&#39;_, _&#39;relu&#39;_, _&#39;leaky_relu&#39;_ and _&#39;gelu&#39;_ are supported</span>
<span class="sd">    prior_sigma_1: float, default = 1.0</span>
<span class="sd">        The prior weight distribution is a scaled mixture of two Gaussian</span>
<span class="sd">        densities:</span>

<span class="sd">        $$</span>
<span class="sd">           \begin{aligned}</span>
<span class="sd">           P(\mathbf{w}) = \prod_{i=j} \pi N (\mathbf{w}_j | 0, \sigma_{1}^{2}) + (1 - \pi) N (\mathbf{w}_j | 0, \sigma_{2}^{2})</span>
<span class="sd">           \end{aligned}</span>
<span class="sd">        $$</span>

<span class="sd">        `prior_sigma_1` is the prior of the sigma parameter for the first of the two</span>
<span class="sd">        Gaussians that will be mixed to produce the prior weight</span>
<span class="sd">        distribution.</span>
<span class="sd">    prior_sigma_2: float, default = 0.002</span>
<span class="sd">        Prior of the sigma parameter for the second of the two Gaussian</span>
<span class="sd">        distributions that will be mixed to produce the prior weight</span>
<span class="sd">        distribution for each Bayesian linear and embedding layer</span>
<span class="sd">    prior_pi: float, default = 0.8</span>
<span class="sd">        Scaling factor that will be used to mix the Gaussians to produce the</span>
<span class="sd">        prior weight distribution ffor each Bayesian linear and embedding</span>
<span class="sd">        layer</span>
<span class="sd">    posterior_mu_init: float = 0.0</span>
<span class="sd">        The posterior sample of the weights is defined as:</span>

<span class="sd">        $$</span>
<span class="sd">           \begin{aligned}</span>
<span class="sd">           \mathbf{w} &amp;= \mu + log(1 + exp(\rho))</span>
<span class="sd">           \end{aligned}</span>
<span class="sd">        $$</span>
<span class="sd">        where:</span>

<span class="sd">        $$</span>
<span class="sd">           \begin{aligned}</span>
<span class="sd">           \mathcal{N}(x\vert \mu, \sigma) &amp;= \frac{1}{\sqrt{2\pi}\sigma}e^{-\frac{(x-\mu)^2}{2\sigma^2}}\\</span>
<span class="sd">           \log{\mathcal{N}(x\vert \mu, \sigma)} &amp;= -\log{\sqrt{2\pi}} -\log{\sigma} -\frac{(x-\mu)^2}{2\sigma^2}\\</span>
<span class="sd">           \end{aligned}</span>
<span class="sd">        $$</span>

<span class="sd">        $\mu$ is initialised using a normal distributtion with mean</span>
<span class="sd">        `posterior_mu_init` and std equal to 0.1.</span>
<span class="sd">    posterior_rho_init: float = -7.0</span>
<span class="sd">        As in the case of $\mu$, $\rho$ is initialised using a</span>
<span class="sd">        normal distributtion with mean `posterior_rho_init` and std equal to</span>
<span class="sd">        0.1.</span>

<span class="sd">    Attributes</span>
<span class="sd">    ----------</span>
<span class="sd">    bayesian_cat_and_cont_embed: nn.Module</span>
<span class="sd">        This is the module that processes the categorical and continuous columns</span>
<span class="sd">    bayesian_tab_mlp: nn.Sequential</span>
<span class="sd">        mlp model that will receive the concatenation of the embeddings and</span>
<span class="sd">        the continuous columns</span>

<span class="sd">    Examples</span>
<span class="sd">    --------</span>
<span class="sd">    &gt;&gt;&gt; import torch</span>
<span class="sd">    &gt;&gt;&gt; from pytorch_widedeep.bayesian_models import BayesianTabMlp</span>
<span class="sd">    &gt;&gt;&gt; X_tab = torch.cat((torch.empty(5, 4).random_(4), torch.rand(5, 1)), axis=1)</span>
<span class="sd">    &gt;&gt;&gt; colnames = [&#39;a&#39;, &#39;b&#39;, &#39;c&#39;, &#39;d&#39;, &#39;e&#39;]</span>
<span class="sd">    &gt;&gt;&gt; cat_embed_input = [(u,i,j) for u,i,j in zip(colnames[:4], [4]*4, [8]*4)]</span>
<span class="sd">    &gt;&gt;&gt; column_idx = {k:v for v,k in enumerate(colnames)}</span>
<span class="sd">    &gt;&gt;&gt; model = BayesianTabMlp(mlp_hidden_dims=[8,4], column_idx=column_idx, cat_embed_input=cat_embed_input,</span>
<span class="sd">    ... continuous_cols = [&#39;e&#39;])</span>
<span class="sd">    &gt;&gt;&gt; out = model(X_tab)</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
        <span class="bp">self</span><span class="p">,</span>
        <span class="n">column_idx</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span>
        <span class="o">*</span><span class="p">,</span>
        <span class="n">cat_embed_input</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
        <span class="n">cat_embed_activation</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
        <span class="n">continuous_cols</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
        <span class="n">embed_continuous</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
        <span class="n">cont_embed_dim</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
        <span class="n">cont_embed_dropout</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
        <span class="n">cont_embed_activation</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
        <span class="n">use_cont_bias</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
        <span class="n">cont_norm_layer</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;batchnorm&quot;</span><span class="p">,</span> <span class="s2">&quot;layernorm&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
        <span class="n">mlp_hidden_dims</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">200</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span>
        <span class="n">mlp_activation</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;leaky_relu&quot;</span><span class="p">,</span>
        <span class="n">prior_sigma_1</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
        <span class="n">prior_sigma_2</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.002</span><span class="p">,</span>
        <span class="n">prior_pi</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.8</span><span class="p">,</span>
        <span class="n">posterior_mu_init</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.0</span><span class="p">,</span>
        <span class="n">posterior_rho_init</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="o">-</span><span class="mf">7.0</span><span class="p">,</span>
        <span class="n">pred_dim</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>  <span class="c1"># Bayesian models will require their own trainer and need the output layer</span>
    <span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">BayesianTabMlp</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">column_idx</span> <span class="o">=</span> <span class="n">column_idx</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">cat_embed_input</span> <span class="o">=</span> <span class="n">cat_embed_input</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">cat_embed_activation</span> <span class="o">=</span> <span class="n">cat_embed_activation</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">continuous_cols</span> <span class="o">=</span> <span class="n">continuous_cols</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">cont_norm_layer</span> <span class="o">=</span> <span class="n">cont_norm_layer</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">embed_continuous</span> <span class="o">=</span> <span class="n">embed_continuous</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">cont_embed_dim</span> <span class="o">=</span> <span class="n">cont_embed_dim</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">cont_embed_dropout</span> <span class="o">=</span> <span class="n">cont_embed_dropout</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">use_cont_bias</span> <span class="o">=</span> <span class="n">use_cont_bias</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">cont_embed_activation</span> <span class="o">=</span> <span class="n">cont_embed_activation</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">mlp_hidden_dims</span> <span class="o">=</span> <span class="n">mlp_hidden_dims</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">mlp_activation</span> <span class="o">=</span> <span class="n">mlp_activation</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">prior_sigma_1</span> <span class="o">=</span> <span class="n">prior_sigma_1</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">prior_sigma_2</span> <span class="o">=</span> <span class="n">prior_sigma_2</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">prior_pi</span> <span class="o">=</span> <span class="n">prior_pi</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">posterior_mu_init</span> <span class="o">=</span> <span class="n">posterior_mu_init</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">posterior_rho_init</span> <span class="o">=</span> <span class="n">posterior_rho_init</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">pred_dim</span> <span class="o">=</span> <span class="n">pred_dim</span>

        <span class="n">allowed_activations</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;relu&quot;</span><span class="p">,</span> <span class="s2">&quot;leaky_relu&quot;</span><span class="p">,</span> <span class="s2">&quot;tanh&quot;</span><span class="p">,</span> <span class="s2">&quot;gelu&quot;</span><span class="p">]</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mlp_activation</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">allowed_activations</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s2">&quot;Currently, only the following activation functions are supported &quot;</span>
                <span class="s2">&quot;for the Bayesian MLP&#39;s dense layers: </span><span class="si">{}</span><span class="s2">. Got &#39;</span><span class="si">{}</span><span class="s2">&#39; instead&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
                    <span class="s2">&quot;, &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">allowed_activations</span><span class="p">),</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">mlp_activation</span><span class="p">,</span>
                <span class="p">)</span>
            <span class="p">)</span>

        <span class="c1"># Categorical</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cat_embed_input</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">cat_embed</span> <span class="o">=</span> <span class="n">BayesianDiffSizeCatEmbeddings</span><span class="p">(</span>
                <span class="n">column_idx</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">column_idx</span><span class="p">,</span>
                <span class="n">embed_input</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">cat_embed_input</span><span class="p">,</span>
                <span class="n">prior_sigma_1</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">prior_sigma_1</span><span class="p">,</span>
                <span class="n">prior_sigma_2</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">prior_sigma_2</span><span class="p">,</span>
                <span class="n">prior_pi</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">prior_pi</span><span class="p">,</span>
                <span class="n">posterior_mu_init</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">posterior_mu_init</span><span class="p">,</span>
                <span class="n">posterior_rho_init</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">posterior_rho_init</span><span class="p">,</span>
                <span class="n">activation_fn</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">cat_embed_activation</span><span class="p">,</span>
            <span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">cat_out_dim</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">([</span><span class="n">embed</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="k">for</span> <span class="n">embed</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">cat_embed_input</span><span class="p">]))</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">cat_out_dim</span> <span class="o">=</span> <span class="mi">0</span>

        <span class="c1"># Continuous</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">continuous_cols</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">cont_idx</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">column_idx</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">continuous_cols</span><span class="p">]</span>
            <span class="k">if</span> <span class="n">cont_norm_layer</span> <span class="o">==</span> <span class="s2">&quot;layernorm&quot;</span><span class="p">:</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">cont_norm</span><span class="p">:</span> <span class="n">NormLayers</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">LayerNorm</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">continuous_cols</span><span class="p">))</span>
            <span class="k">elif</span> <span class="n">cont_norm_layer</span> <span class="o">==</span> <span class="s2">&quot;batchnorm&quot;</span><span class="p">:</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">cont_norm</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm1d</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">continuous_cols</span><span class="p">))</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">cont_norm</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Identity</span><span class="p">()</span>
            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">embed_continuous</span><span class="p">:</span>
                <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">cont_embed_dim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> <span class="p">(</span>
                    <span class="s2">&quot;If &#39;embed_continuous&#39; is True, &#39;cont_embed_dim&#39; must be &quot;</span>
                    <span class="s2">&quot;provided&quot;</span>
                <span class="p">)</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">cont_embed</span> <span class="o">=</span> <span class="n">BayesianContEmbeddings</span><span class="p">(</span>
                    <span class="n">n_cont_cols</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">continuous_cols</span><span class="p">),</span>
                    <span class="n">embed_dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">cont_embed_dim</span><span class="p">,</span>
                    <span class="n">prior_sigma_1</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">prior_sigma_1</span><span class="p">,</span>
                    <span class="n">prior_sigma_2</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">prior_sigma_2</span><span class="p">,</span>
                    <span class="n">prior_pi</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">prior_pi</span><span class="p">,</span>
                    <span class="n">posterior_mu_init</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">posterior_mu_init</span><span class="p">,</span>
                    <span class="n">posterior_rho_init</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">posterior_rho_init</span><span class="p">,</span>
                    <span class="n">use_bias</span><span class="o">=</span><span class="p">(</span>
                        <span class="kc">False</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_cont_bias</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_cont_bias</span>
                    <span class="p">),</span>
                    <span class="n">activation_fn</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">cont_embed_activation</span><span class="p">,</span>
                <span class="p">)</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">cont_out_dim</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">continuous_cols</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">cont_embed_dim</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">cont_out_dim</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">continuous_cols</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">cont_out_dim</span> <span class="o">=</span> <span class="mi">0</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">output_dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cat_out_dim</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">cont_out_dim</span>

        <span class="n">mlp_hidden_dims</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">output_dim</span><span class="p">]</span> <span class="o">+</span> <span class="n">mlp_hidden_dims</span> <span class="o">+</span> <span class="p">[</span><span class="n">pred_dim</span><span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">bayesian_tab_mlp</span> <span class="o">=</span> <span class="n">BayesianMLP</span><span class="p">(</span>
            <span class="n">mlp_hidden_dims</span><span class="p">,</span>
            <span class="n">mlp_activation</span><span class="p">,</span>
            <span class="kc">True</span><span class="p">,</span>  <span class="c1"># use_bias</span>
            <span class="n">prior_sigma_1</span><span class="p">,</span>
            <span class="n">prior_sigma_2</span><span class="p">,</span>
            <span class="n">prior_pi</span><span class="p">,</span>
            <span class="n">posterior_mu_init</span><span class="p">,</span>
            <span class="n">posterior_rho_init</span><span class="p">,</span>
        <span class="p">)</span>

    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_embeddings</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bayesian_tab_mlp</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">x</span>

    <span class="k">def</span> <span class="nf">_get_embeddings</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
        <span class="n">tensors_to_concat</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cat_embed_input</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">x_cat</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cat_embed</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
            <span class="n">tensors_to_concat</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x_cat</span><span class="p">)</span>

        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">continuous_cols</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">x_cont</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cont_norm</span><span class="p">((</span><span class="n">X</span><span class="p">[:,</span> <span class="bp">self</span><span class="o">.</span><span class="n">cont_idx</span><span class="p">]</span><span class="o">.</span><span class="n">float</span><span class="p">()))</span>
            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">embed_continuous</span><span class="p">:</span>
                <span class="n">x_cont</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cont_embed</span><span class="p">(</span><span class="n">x_cont</span><span class="p">)</span>
                <span class="n">x_cont</span> <span class="o">=</span> <span class="n">einops</span><span class="o">.</span><span class="n">rearrange</span><span class="p">(</span><span class="n">x_cont</span><span class="p">,</span> <span class="s2">&quot;b s d -&gt; b (s d)&quot;</span><span class="p">)</span>
            <span class="n">tensors_to_concat</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x_cont</span><span class="p">)</span>

        <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">tensors_to_concat</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">x</span>
</code></pre></div></td></tr></table></div>
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